Principal Scientist, AI-Driven Oncology Target Discovery

Principal Responsibilities

• Drive integration of computational approaches into cross-functional target discovery workflows, partnering with functional genomics, discovery biology, and computational biology teams to inform target nomination and portfolio decisions.
• Lead the design and deployment of agentic AI and machine learning systems to enable scalable, end-to-end workflows for target identification, prioritization, and validation.
• Integrate and analyze multimodal, patient-derived datasets (omics, functional screens, real-world data) to uncover disease biology, resistance mechanisms, and patient stratification opportunities that inform target selection.
• Translate complex analytical outputs into clear, decision-ready insights to support Go/No-Go recommendations and guide target discovery strategy.
• Author scientific reports and present methods, results, and conclusions to a publishable standard.

Qualifications

• Bachelor's Degree in Computational Biology, Systems Biology, Computer Science, Machine Learning, Statistics, or a related quantitative field, or Master's Degree with 8+ years of relevant industry experience, or PhD with 4+ years of relevant industry experience.
• Deep expertise in computational target identification approaches, including functional genomics, perturbation screens, single-cell and spatial omics, and real-world patient data.
• Demonstrated experience developing and deploying applied AI agents and/or complex LLM-driven applications in research or industry settings.
• Excellent collaboration and leadership skills, with a track record of influencing cross-functional teams and operating effectively in fast-paced, ambiguous environments.
• Strong grounding in systems biology and disease biology within drug discovery, preferably in oncology, with experience translating computational findings into therapeutic hypotheses.
• Prominent scientific impact, with a strong publication record in computational biology, machine learning, or bioinformatics, and the ability to communicate complex methodologies and insights effectively.